Authors:Med Karim Abdmouleh, Ali Khalfallah, Med Salim BouhlelPages: 340 - 350Abstract: Ensuring the confidentiality of exchanged data is always a great concern for any communication. Also, the purpose of compression is to reduce the amount of data while preserving important information. This reduction leads to the archiving of more information on the same storage medium and minimises the transfer times via telecommunication networks. Indeed, the combination of encryption and compression guarantees both confidentiality and authentication of information. In addition, it reduces processing time and transmission on public channels and increases storage capacity. In this paper, we propose a new approach of a partial or selective encryption for medical Images based on the discrete wavelet transform (DWT) coefficients and compatible with the norm JPEG2000. The obtain results prove that, the proposed scheme provides a significant reduction of the processing time during the encryption and decryption, without tampering the high compression rate of the compression algorithm.Keywords: crypto-compression; encryption; compression; discrete wavelet transform; DWT; RSA; JPEG2000; telemedicineCitation: International Journal of Computational Vision and Robotics, Vol. 9, No. 4 (2019) pp. 340 - 350PubDate: 2019-08-12T23:20:50-05:00DOI: 10.1504/IJCVR.2019.101536Issue No:Vol. 9, No. 4 (2019)

Authors:Benjamin Bird, Thomas Wright, Simon Watson, Barry LennoxPages: 368 - 386Abstract: In this paper we propose and demonstrate a novel void characterisation algorithm which is able to distinguish between internal and external voids that are present in point clouds of both manifold and non-manifold objects and 3D scenes. We demonstrate the capabilities of our algorithm using several point clouds representing both scenes and objects. Our algorithm is shown in both a descriptive overview format as well as pseudocode. We also compare a variety of different void detection algorithms and then present a novel refinement to the best performing of these algorithms. Our refinement allows for voids in point clouds to be detected more efficiently, with fewer false positives and with over an order of magnitude improvement in terms of run time. We show our run time performance and compare it to results obtained using alternative algorithms, when tested using popular single board computers. This comparison is important as our work is intended for online robotics applications, where hardware is typically of low computational power. The target application for this work is 3D scene reconstruction to aid in the decommissioning of nuclear facilities.Keywords: point cloud; void detection; meshing; reconstruction; computer visionCitation: International Journal of Computational Vision and Robotics, Vol. 9, No. 4 (2019) pp. 368 - 386PubDate: 2019-08-12T23:20:50-05:00DOI: 10.1504/IJCVR.2019.101538Issue No:Vol. 9, No. 4 (2019)

Authors:Van-Hung Le, Hai Vu, Thuy Thi Nguyen, Thi-Lan Le, Thanh-Hai TranPages: 387 - 411Abstract: Estimating parameters of a primitive shape from a point cloud data is a challenging problem due to the data containing noises and computational time demand. In this paper, we present a new robust estimator (named GCSAC, geometrical constraint sample consensus) aimed at solving such issues. The proposed algorithm takes into account geometrical constraints to construct qualified samples for the estimation. Instead of randomly drawing minimal subset of sample, explicit geometrical properties of the interested primitive shapes (e.g., cylinder, sphere and cone) are used to drive the sampling procedures. Based on the collected samples, model estimation and verification procedures of the robust estimator are deployed in GCSAC. Extensive experiments are conducted on synthesised and real datasets. Comparing with the common algorithms of RANSAC family, GCSAC outperforms in term of both the precision of the estimated model and computational time. The implementations of GCSAC and the datasets are made publicly available.Keywords: robust estimator; primitive shape estimation; random sample consensus; RANSAC and RANSAC variations; quality of samples; point cloud dataCitation: International Journal of Computational Vision and Robotics, Vol. 9, No. 4 (2019) pp. 387 - 411PubDate: 2019-08-12T23:20:50-05:00DOI: 10.1504/IJCVR.2019.101539Issue No:Vol. 9, No. 4 (2019)